GCRI · Biotechnology and Biosecurity
Probability and statistics for biotechnology and biosecurity
How probability and statistics is applied in Biotechnology and Biosecurity. Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline.
Probability and statistics for biotechnology and biosecurity on the record.
- MethodProbability and statistics
- Applied inBiotechnology and Biosecurity
- Data5 inputs
- ChecksValidation
- Canonical pageSTEM Commons
What probability and statistics does here.
Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In health and biosecurity and food and agriculture, statistical methods quantify the likelihood and uncertainty of losses across interconnected systems. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on the STEM Commons.
- 01
Methods used here
statistical inference; Bayesian analysis; regression modelling; sampling design; hypothesis testing; resampling methods.
- 02
Validation
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Tail fit.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Tail fit
Biotechnology and biosecurity · Probability and statistics · Run R-0830
Return level curve
Method card
- Method
- Probability and statistics
- Approach
- peaks over threshold
- Threshold
- 93th percentile
- Label
- MODELLED
Parameters
generalised Pareto fit
| Shape ξ | 0.20 | 0.00 | 0.41 | heavy tail if above zero |
| Scale σ | 2.0 | 1.0 | 7.5 | units of the variable |
| Exceedances | 98 | declustered |
What the method consumes.
Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Designed surveys and experiments | Input | OBSERVED |
| Observational datasets | Input | OBSERVED |
| Administrative records | Input | OBSERVED |
| Sensor time series | Input | OBSERVED |
| Simulation outputs | Input | OBSERVED |
SEIR on a network of places.
An outbreak seeded in one place spreads through a small-world network by the share of contacts people make elsewhere; play it to see the order of arrival and how the timing of measures moves the peak. Teaching model with stated illustrative values, not a prediction.
Controls
- Basic reproduction number R0
- 2.5
- Latent period
- 3.0 days
- Infectious period
- 5.0 days
- Share of contacts made in other places
- 8%
- Places in the network
- 24
- Measures start on day
- 40
- Contact reduction from measures
- 40%
- Days simulated
- 240 days
Readouts
- Peak new infections
- 5.28 per 1,000 a day
- Day of the peak
- 148
- Share ever infected
- 58%
- Places reached
- 100%
- Reproduction number under measures
- 1.50
Numbers, methods and sources
| Day | New infections per 1,000 | Cumulative infected (%) |
|---|---|---|
| 0 | 0.00 | 0.0 |
| 17 | 0.03 | 0.0 |
| 34 | 0.32 | 0.2 |
| 51 | 0.96 | 1.4 |
| 68 | 1.74 | 3.7 |
| 85 | 2.72 | 7.5 |
| 102 | 3.74 | 13.1 |
| 119 | 4.56 | 20.2 |
| 136 | 5.11 | 28.5 |
| 153 | 5.24 | 37.4 |
| 170 | 4.44 | 45.7 |
| 187 | 2.91 | 52.0 |
| 204 | 1.52 | 55.6 |
| 221 | 0.69 | 57.3 |
| 238 | 0.30 | 58.1 |
Methods
- SEIR compartments Kermack and McKendrick (1927), Proc. R. Soc. A 115
- Metapopulation coupling by mobility Balcan and others (2009), PNAS 106
- Small-world network Watts and Strogatz (1998), Nature 393
- Modelling infectious diseases Keeling and Rohani (2008), Princeton University Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Cities Natural Earth
- Airports Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Biotechnology and Biosecurity in Foundry
- Tested at Biotechnology and Biosecurity in Universe
- Codified in Health and Biosecurity in Standards
- Published in Biotechnology and Biosecurity in Reports
- Taught in Faculty of Health and Society in Academy
How results are checked.
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
The model, the data and the uncertainty.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for probability and statistics for biotechnology and biosecurity could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
Topics that rely on this method.
Who maintains the method.
Systems Science and Modelling stewards probability and statistics for the Consortium and reviews how it is applied across platforms.
Where this page leads across the Consortium.
Questions and answers.
What are the limits of probability and statistics in this field?
How are results checked?
Are the values on this page real?
Where else is this method applied?
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